Biexponential fitting for noisy data with error propagation

Biexponential time-series models commonly find use in biophysics, biochemistry and pharmacokinetics. Indeed, reactions that are described by biexponential functions typical for many biological processes. The kinetics of these modelled transcendental irrational equations interconnecting the reagent concentrations, time rate constants. is apparently a case nonlinear regression, as such, very often estimate its parameters obtained with methods software tools performing fits. first problem user encounters when using techniques consists having to provide not too inaccurate intervals within which can vary. Providing arbitrary initial guesses on parameter ranges fit procedure causes nonconvergence. second need obtain an error interval due propagation experimental affects measurements dependent variable. In this study, we propose extension well-established mathematical method based linearization integral efficient unsupervised estimation function. Our integration model from variable estimates. There three main innovative contributions work: (1) made regression available practical applications; (2) provided complex operations, such integration, matrix inversion multiplication; (3) calibration dynamics (i) water desorption small ligand surface where two types binding sites present (ii) decrease determinant viability organs sustaining ischaemic injury before transplantation.